Objective Aiming at problems such as low accuracy in classification prediction by conventional data processing an Optuna-MLP-LightGBM combination model for predicting the properties of anticancer candidate drugs was proposed. Methods A total of 1 974 compounds 729 molecular descriptors for each compound were collected. Firstly a multi-layer perceptron MLP was used to aggregate high-dimensional data. A jump connection was used to realize the width processing of the data. The output data and input data were merged to form a width data set. This enhanced feature recognition and prevented the loss of useful information thereby improving information flow. Then LightGBM replaced the classification layer in the MLP neural network for better classification and to avoid overfitting issues. Finally theMLP-LightGBM classification prediction model based on Optuna optimization was constructed to predict the classification of the permeability of the small intestinal epithelial cells of the candidate drug Caco-2 . Results The accuracy AUC and F1 values of the model reached 91. 03% 97. 31 % and 90. 48 % respectively. Through ablation experiments it was found that the model?? s classification performance has been improved compared with the MLP model after implementing data width processing and classification with MLP-LightGBM with increases of 0. 51% 1. 22% and 0. 7% in the three metrics respectively. Compared with traditional models such as Logistic Regression LR Attentive FP and MLP this model can better integrate data information with average growth rates compared with the base model of 5. 94% 5. 65% and 6. 56% respectively. Conclusion The jump-join processing enables the MLP network to effectively extract features and expand datasets. Introducing machine learning can further improve classification accuracy. Therefore it can become an important auxiliary tool in high-throughput drug screening.